Remote Data Engineering Lead & Cloud Infra Advisor
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Job summary
Chevron Global Business Services (GBS) in Buenos Aires, Argentina seeks an Executive Director, Data Engineering to lead a 25-engineer network data team. You will own the data platform end-to-end, setting governance and strategy for trusted, production-ready data used by automation, analytics, and AI.
You will also serve as Argentina IP Foundational Services site lead, aligning regional efforts with global priorities, mentoring talent, and driving governance, risk, and cross-functional
Qualifications
- 10+ years in data engineering or data platform engineering with leadership experience.
- Proven ownership of a production data platform: pipelines, modelling, storage, and serving.
Responsibilities
- Define and execute the network data strategy and data modelling.
- Build and operate the data platform with ingestion/streaming pipelines and serving APIs.
- Establish data models and data contracts with data quality targets.
- Drive data lineage, observability, and governance in collaboration with teams.
- Make network data AI-ready for agentic and LLM-driven capabilities.
Skills
Education
Tools
Job description
The Executive Director, Data Engineering is the functional leader for network data within Global Network Services, directly leading a 25 person engineering organization focuses on Network Data. Data is the foundation layer of the platforms we build: automation, observability, analytics, and AI all depend on trustworthy, queryable network state. This leader owns the network data platform end to end, from sourcing and modelling through to the serving layer that engineering teams consume, and sets the strategy for how network data is governed, delivered, and continuously improved at scale.
In parallel, the Executive Director serves as the Argentina (Buenos Aires) Regional Lead for Infrastructure Platform Foundational Services (Network, Storage, DataCentre, Data Protection and Recovery). Accountable for site strategy, talent development, governance, executive presence, and operational alignment across the region. The role establishes Argentina as a strategic engineering hub by building and sustaining high-performing engineering teams aligned to global priorities and outcomes.
This leader partners closely with the automation platform, SRE, and Product Engineering teams, and with firm-wide data governance and data quality programmes, to ensure network data is well governed and fit for production use.
- Define and execute the network data strategy, covering data modelling, authoritative sources, distribution, and how data is made available to consuming platforms
- Build and operate the data platform: ingestion and streaming pipelines, transformation, storage and modelling, and performant serving APIs and self‑service access for engineering teams
- Establish consistent data models and identity standards for network entities (device, circuit, site, interface) so data can be reliably joined and reused across systems
- Drive data quality as an engineering discipline: validation, automated testing, monitoring, and measurable quality targets across the data lifecycle
- stablish data contracts and data SLOs covering freshness, completeness, accuracy, and availability, with clear ownership, versioning, and schema‑evolution standards
- Implement lineage and observability across data pipelines so issues can be traced to source and change impact can be assessed ahead of time
- Partner with firm‑wide data governance and data quality programmes to meet audit, risk, and regulatory obligations
- Make network data AI‑ready: structured, well‑described, and accessible so agentic and LLM‑driven capabilities can reason over accurate state, and apply AI within the data lifecycle itself
- Establish engineering standards, testing frameworks, and CI/CD pipelines so data pipelines and services are production‑grade, secure‑by‑design, and observable
Deliver measurable outcomes: data SLO attainment, improved data quality metrics, faster access to network data, and demonstrable downstream gains in automation, reliability, and analytics.
- Represent the Argentina hub in global Infrastructure Platforms Foundational Services forums.
- Drive site culture, retention, and consistent engineering standards across functional silos
- Ensure effective cross‑functional collaboration with security, infrastructure, application teams, service management, and business partners to deliver integrated outcomes
- Manage regional resource planning and budget inputs: capacity forecasting, skills coverage, on‑call sustainability, and investment recommendations tied to measurable service improvements
- Hire, develop, and retain engineering talent across the Argentina footprint
Ensure governance, risk, and audit compliance for in‑country operations
- Strong ownership of data platform and data quality outcomes
- Ability to build and scale engineering teams as a manager‑of‑managers across functional and matrixed reporting lines
- Drives a product‑led approach to data, with clear ownership, published standards, and consumers treated as customers
- Strong executive communication skills, including the ability to make the business case for foundational data investment
- Maintains compliance and control discipline
Credible senior technology presence in‑region, able to represent JPMC externally with regulators, universities, and partners
- 10+ years in data engineering or data platform engineering, including leadership roles running data systems at scale and manager‑of‑managers experience
- Proven ownership of a production data platform: pipelines, modelling, storage, and serving, with accountability for freshness, correctness, and availability
- Demonstrated experience establishing data governance in practice: ownership models, data contracts, data quality measurement, lineage, and schema evolution
- Strong hands‑on engineering foundation (Python, SQL, APIs, streaming and batch pipelines, modern data stores) and the judgment to hold a high engineering bar
- Proven track record adopting agentic AI and LLM‑driven capability in production engineering environments, and preparing data estates for AI consumption
- Experience operating within formal risk and control frameworks: audit engagement, evidence quality standards, and remediation governance
- Proven ability to scale teams and drive adoption of common data standards across partner teams
- Strong global collaboration skills across distributed, matrixed organizations
- Prior site, country, or regional engineering hub leadership experience
BS/BA degree or equivalent practical experience in technology, engineering, or a related discipline
- Infrastructure or network domain experience: inventory, topology, configuration, and telemetry data. A strong plus, not a gate
- Experience with source‑of‑truth platforms, or graph and topology data models
- Experience with streaming telemetry and time‑series data at scale
- Financial services or other regulated environment experience
- Experience supporting automation and reliability engineering teams as primary data consumers
- Demonstrated use of AI to redesign data engineering workflows for measurable impact, and to build organizational AI fluency
- Demonstrated experience in incident, problem, and change governance, including executive communications during high‑severity events
- Direct Org: 25 engineers (network data engineering), globally distributed
- Growth aligned to the Network Services data platform build‑out
- Partnership: strong partnership with the automation platform and SRE teams, global Product Engineering, firm‑wide data governance and data quality programmes, and regional Infrastructure leadership
- Regional (matrix): site leadership for all IP Foundational Services engineers in Argentina
Buenos Aires, Buenos Aires, Argentina
Full time
R
Utilizes software engineering principles to deploy and maintain fully automated data transformation pipelines that combine a large variety of storage and computation technologies to handle a distribution of data types and volumes in support of data architecture design.
Job Description
GBS Chevron Global Business Services (GBS), located in Buenos Aires (Puerto Madero), Argentina, is accepting applications for the position of Data Engineer. Successful candidates will join the IT Organization, which is part of a multifunction service and technical center with a workforce of more than 1800 employees that deliver business services and solutions to the corporation across the globe.
Utilizes software engineering principles to deploy and maintain fully automated data transformation pipelines that combine a large variety of storage and computation technologies to handle a distribution of data types and volumes in support of data architecture design.
Responsibilities include:
+ Understand business use of data and stakeholder requirements to support work processes and strategic business objectives
+ Leverage data, software engineering, and data science techniques to create business value through data accessibility. Includes data ingestion, data preparation and analytics processing.
+ Identify, acquire, cleanse & prepare, store data and develop data products aligned with defined architecture patterns
+ Responsible for normalizing and ingesting data from multiple sources.
+ Enable data scientists, making data available for advanced analytics models, and contributing to models.
+ Work with ML Engineer to scale and deploy solution including model, documentation, training, integration
+ Contributing to the inner source development of foundational tools, and/or the deployment of technical services.
Required Qualifications:
+ BS in Computer Science, Management Information Systems, Computer Engineer or related fields or equivalent experience.
+ +5 years in Data Engineer Role
+ Knowledge and/or experience with: data acquisition, preparation and validation; data movement and transformation; analytics solution architecture; big data computing; cloud computing, core data architecture; information security technologies and software engineering using technologies such as: Azure Data Factory (ETL), Databricks, DevOps, CI/CD Pipeline Deployment, Python, Java, Ansible, Azure SQL, Azure Synapse, GIT, ADO, Azure Data Lake, Azure Analysis Services, Logic Apps, Microsoft Data Flows, Kimball Data Warehousing Methodology
+ Analytical thinking, Consulting, Critical Thinking
Relocation Options:
Relocation may be considered.
International Considerations:
Expatriate assignments will not be considered
Chevron regrets that it is unable to sponsor employment Visas or consider individuals on time‑limited Visa status for this position.
Chevron participates in E‑Verify in certain locations as required by law.
Chevron Corporation is one of the world's leading integrated energy companies. Through its subsidiaries that conduct business worldwide, the company is involved in virtually every facet of the energy industry. Chevron explores for, produces and transports crude oil and natural gas; refines, markets and distributes transportation fuels and lubricants; manufactures and sells petrochemicals and additives; generates power; and develops and deploys technologies that enhance business value in every aspect of the company's operations. Chevron is based in Houston, Texas. More information about Chevron is available at .
Chevron is an Equal Opportunity / Aff…
If you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you!
We are looking for a Mid-level Data Engineer to build and maintain dbt pipelines and Snowflake data marts behind customer‑facing reporting, spend analytics, and AI features.
The mandatory requirements are solid dbt experience (modeling, tests, macros, and incremental models), ingestion and orchestration tools such as Airbyte or Prefect, 3+ years in data or analytics engineering on production pipelines, strong SQL and Python, dimensional modeling, Snowflake or a similar cloud warehouse, and an upper‑intermediate English level.
- Build and maintain the dbt pipelines, data marts, and warehouse models that serve in‑app reporting, spend analytics, AI features, and internal analytics, treating them as production systems.
- Translate complicated procurement business logic into maintainable, well‑tested pipeline code, and notice when a model is valid‑looking but wrong.
- Build models within our data mart architecture, following the chapter's conventions for grain, conformed dimensions, and incremental patterns, and propose improvements when you find them.
- Contribute to and help buildout the semantic layer that our internal MCP servers and AI agents query, so metrics and dimensions are defined once and answered consistently.
- Maintain the boundary between PII and agent/reporting‑safe data.
- Build and maintain the FastAPI services that expose reporting marts to customers.
- Build and maintain ingestion pipelines (Airbyte, OneSchema, S3) into the warehouse, and support our Forward Deployed Engineering function in bringing customer data into our systems.
- Implement data quality checks: freshness, schema and reconciliation tests, and alerting, so problems surface before customers find them.
- Implement metric definitions faithfully, and flag conflicting or ambiguous definitions to the chapter rather than guessing.
- Keep documentation and lineage current so that humans and AI agents can both find and trust what's in the warehouse.
- Tune Snowflake performance and cost within the models you build.
- Use agentic coding tools in your daily work, within the guardrails (tests, conventions, review patterns) the team maintains, and understand why those guardrails exist.
- Share what you learn through code review and team discussion.
- Communicate clearly with engineering, product, and customer‑facing teams about what the data means and what it can and can't do.
- 3+ years in analytics engineering, data engineering, or a closely related role, including hands‑on work on production data pipelines that other systems or customers depended on.
- Solid dbt experience: modeling, tests, macros, and incremental models.
- Strong SQL and working Python.
- Data modeling fundamentals: dimensional modeling, data marts, grain and conformance.
- Experience with a modern cloud warehouse (Snowflake preferred).
- Experience with ingestion tooling and orchestration (Airbyte, Prefect, or equivalents).
- Familiarity with agentic coding tools and an understanding of why guardrails around their output matter.
- Deep curiosity about the business: you want to understand what the data means and how customers use it, not just how it flows.
- Software engineering discipline for analytics code: Git, code review, CI/CD.
- Can take a multi‑sprint piece of work from spec to production with normal oversight, and can explain tradeoffs to technical and non‑technical stakeholders.
- Experience building or maintaining API services on top of warehouse data (FastAPI or similar).
- Experience with semantic layer or governed metric definitions that multiple consumers rely on, including for LLM‑driven or agent‑driven querying.
- Experience exposing warehouse data to customers through embedded analytics or customer‑facing APIs.
- Experience with data observability tooling.
- Experience supporting customer data onboarding or implementation teams.
- Experience deploying data pipelines across multiple regions and managing PII or sensitive data.
- Knowledge of procurement and/or supply chain data models.
- Prior experience in enterprise software and startups.
- Professional growth : Mentorship, TechTalks, and personalized growth roadmaps.
- Competitive compensation : USD-based pay with education, fitness, and team activity budgets.
- Exciting projects : Modern solutions with Fortune 500 and top product companies.
- Flextime : Flexible schedule with remote and office options.
We have an exciting and rewarding opportunity for you to take your data engineering career to the next level.
- Executes software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
- Collaborates with software engineers and teams to design, develop, test, and implement availability, reliability, scalability, and solutions in their applications
- Gathers, analyzes, synthesizes, and develops visualizations and reporting from large, diverse data sets in service of continuous improvement of software applications and systems
- Proactively identifies hidden problems and patterns in data and uses these insights to drive improvements to coding hygiene and system architecture
- Delivers data collection, storage, access, and analytics data platform solutions in a secure, stable, and scalable way.
- Supports review of controls to ensure sufficient protection of enterprise data
- Contributes to data and software engineering communities of practice and events that explore new and emerging technologies
- Formal training or relevant data engineering certification and 5+ years applied experience
- Experience in Qlik Sense in both ETL and Dashboard development
- In depth knowledge of Qliksense scripting, QVD Files, Set Analysis
- Practical experience in full stack projects, hands‑on mainly with backend technologies such as: Java, Spring Boot, JPA (Java Persistence API) Spring Data Repositories
- Experience in developing, debugging, and maintaining code in a corporate ecosystem with one or more modern programming languages and database querying languages
- Exposure to agile methodologies such as CI/CD, Application Resiliency, and Cybersecurity best practices
- Experience with SQL relational DB technologies such as MySQL, Oracle RDMS.
- Knowledge of Web Frameworks (. React) will be an added advantage.
- Exposure to public cloud technologies, AWS/GCP/Azure experience or certifications is a plus
- Experience in observability, monitoring, and telemetry collection using tools such as Grafana, Dynatrace, Prometheus, Datadog, Splunk, and others.
- Familiarity with industry‑wide technology trends and best practices
LangGraph.
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Argentina Full‑time
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